Collaborative ML Incentivization Using CVAE Contribution Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for measuring data contribution in collaborative machine learning face challenges such as high computational complexity, the need for additional training, and the risk of attackers compromising model integrity, without adequately addressing incentivization and resource optimization.
Innovation Solution
A system using a pre-trained Conditional Variational Auto Encoder (CVAE) to determine an optimum score for each client device, compute contribution scores, and cluster devices into classes based on grading scores, eliminating attackers, and updating the global model with selected client devices to provide incentives based on data quality and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Shapley value metric is used to measure data contribution, then contribution measurement is achieved, but computational complexity becomes exponential
Solution Approach 1:
The patent transforms the contribution measurement problem from exact Shapley value computation to an estimation problem using neural network parameters. By changing the measurement approach from combinatorial calculation to parameter-based estimation, the computational complexity is reduced from exponential to polynomial while maintaining acceptable measurement precision.
Solution Approach 2:
The patent introduces an intermediary neural network model that learns to predict contribution values based on local model parameters. This intermediary model acts as a bridge between the complex Shapley value calculation and the final contribution measurement, avoiding direct computation of exponential complexity while providing accurate estimates.
2Measurement precision
If additional training is performed for every sub-dataset to compute Shapley value, then accurate contribution measurement is achieved, but time consumption increases significantly
Solution Approach 1:
The patent performs preliminary training of a neural network model on historical data and local model parameters before actual contribution measurement. This pre-trained model can then quickly estimate contributions for new data samples without requiring time-consuming retraining, significantly reducing the time loss while maintaining measurement accuracy.
Solution Approach 2:
The patent creates a copy of the contribution measurement function in the form of a trained neural network model. Instead of performing the original complex training process for every new sub-dataset, the system uses the copied model to make rapid predictions, achieving the same measurement goal with fraction of the time.
3Manufacturing precision
If edge devices perform local processing to obtain optimum models, then model quality improves, but additional computation is required from edge devices
Solution Approach 1:
The patent enables each edge device to perform localized processing to obtain its own optimum model tailored to its local data characteristics. By allowing local optimization rather than centralized processing, the system improves model quality for each device while distributing the computational energy consumption across multiple devices rather than concentrating it in one location.
4Ease of operation
If conventional contribution measurement methods are used, then participation incentive is provided, but attacker detection capability is insufficient
Solution Approach 1:
The patent makes the neural network-based contribution measurement system multi-functional by enabling it to both incentivize participation and detect attackers. The same model that estimates contribution values also identifies anomalous patterns indicative of malicious behavior, eliminating the need for separate mechanisms and improving reliability without compromising ease of operation.
Data Source
AI summary
Systems and methods for smart incentivization for achieving collaborative machine learning are disclosed. A system receives local model parameters from plurality of client devices in a network, for global model corresponding to collaborative machine learning. The system determines an optimum score for each client device using pre-trained Conditional Variational Auto Encoder (CVAE), based on local model parameter. The system computes contribution score for each client device by determining relative distance value of optimum score corresponding to each client device with optimum score corresponding to another client device from the plurality of client devices, and a global model optimum score of global model. The system updates global model with local model parameter received from the selected set of client devices of the plurality of client devices corresponding to good class, average class, and bad class. The system outputs grading score, an incentive, importance score for each of selected client devices, and a performance of the global model.